library(readr)
library(ggplot2)
library(dplyr)
library(rms)
library(synthpop)
source('./helper functions.R')

Reading in original dataset, performing multiple imputation and writing out imputed dataset. This chunk commented out because we’ve already done this and saved the resulting file as our starting point.

Read in dataset from Brinati paper. Dataset already been imputed based on code above.

require(readr)
brinati_covid_study_v2.imputed <- read_csv("data/brinati-covid_study_v2_imputed.csv",
  col_types = cols(GENDER = col_factor(levels = c("M",
  "F")), SWAB = col_factor(levels = c("0",
  "1"))))
p<-plot_model(glm.orig.fit)
p<-plot_model(glm.orig.fit)
p<-ApplyFigureThemeLargeFontOnly(p + ylim(.1, 2.5) ) 
Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.

Draw ROC curve

p <- ff %>% mutate(fpr=1-Specificity) %>%
  ggplot(., aes(x=fpr, y=Sensitivity)) + geom_point(aes(frame=Cutoff)) +
    xlab('False Positive Rate (1-Specificity)') + ylab('True Positive Rate\n(Sensitivity)')
Ignoring unknown aesthetics: frame
ggplotly(p)

Draw calibration

Calculate hosmer lemeshow statistics

require(performance)
hl.org <- performance_hosmer(glm.orig.fit)
hl.org
# Hosmer-Lemeshow Goodness-of-Fit Test

  Chi-squared: 5.333
           df: 8    
      p-value: 0.722
Summary: model seems to fit well.

Create smoothed cal curve from Van hoorde et al

p<-CreateSmoothedCalCurvePlot(predicted, observed)
p<-ApplyFigureThemeLargeFontOnly(p)
SaveStdSquareFigure(p, 'figs/smoothed-cal-curve-orig-data.png')
p

Boot strap performance in the original dataset

Generate new a synthetic dataset with a prevalence of 10%

#Assess performance of the original model on new prevalence 10 data
assessPerf(predicted = plogis(predict(glm.orig.fit, newdata=as.data.frame(brinati.syn.prev10))), 
           observed = brinati.syn.prev10$SWAB)
          Dxy       C (ROC)            R2             D      D:Chi-sq           D:p             U      U:Chi-sq 
 6.462721e-01  8.231360e-01 -1.737270e+00 -6.089445e-01 -1.688955e+02            NA  7.433448e-01  2.093932e+02 
          U:p             Q         Brier     Intercept         Slope          Emax           E90          Eavg 
 0.000000e+00 -1.352289e+00  2.037312e-01 -2.471404e+00  6.986839e-01  5.722161e-01  5.415682e-01  3.070988e-01 
          S:z           S:p 
 3.691032e+00  2.233457e-04 

Generate new a synthetic dataset with a prevalence of 63% – close to the original dataset

#Assess performance of the original model on new prevalence 10 data
assessPerf(predicted = plogis(predict(glm.orig.fit, newdata=as.data.frame(brinati.syn.prev10))), 
           observed = brinati.syn.prev10$SWAB)
          Dxy       C (ROC)            R2             D      D:Chi-sq           D:p             U      U:Chi-sq 
 7.079320e-01  8.539660e-01 -7.403297e-01 -3.535701e-01 -9.764606e+01            NA  5.720065e-01  1.615898e+02 
          U:p             Q         Brier     Intercept         Slope          Emax           E90          Eavg 
 0.000000e+00 -9.255766e-01  1.923112e-01 -2.097882e+00  7.796140e-01  4.378514e-01  4.367189e-01  2.627114e-01 
          S:z           S:p 
 3.977260e+00  6.971398e-05 

Boot strap performance of a complete re-moel of a prevalence 10% dataset

  1. Look at performance as a function of prevalence
prevalences = seq(0.01, 0.99, 0.01)
dfs_prevs <- lapply(prevalences, function (prev) brinati.syn.factory(prev = prev))
dfs.prevs.perf <- lapply(dfs_prevs, function(df) {
  assessPerf(predicted = plogis(predict(glm.orig.fit, newdata=as.data.frame(df))), observed = df$SWAB)
})

Calculate the different validation measures


dfs.prevs.perf.df <- data.frame(matrix(unlist(dfs.prevs.perf), nrow = length(prevalences), byrow = TRUE))
colnames(dfs.prevs.perf.df) <- names(dfs.prevs.perf[[1]])
dfs.prevs.perf.df$Prevalence = prevalences
dfs.prevs.perf.df <- gather(dfs.prevs.perf.df, Measure, Value, Dxy:`S:p`, factor_key = TRUE)

Plot the distribution of validation measures

measures = c('C (ROC)', 'Brier', 'Intercept', 'Slope')
ggplot(dfs.prevs.perf.df %>% filter(Measure %in% measures), aes(x=Measure, y=Value,  color=Measure)) + geom_boxplot() +
  geom_point(data=data.frame(Measure = measures, Value = c(1, 0, 0, 1)), size=3, color='blue') + theme_bw()


ggplot(dfs.prevs.perf.df %>% filter(Measure %in% measures), aes(x=Prevalence, y=Value,  color=Measure, group=Measure)) + geom_line() + theme_bw()

---
title: "R Notebook"
output: html_notebook
---
```{r message=FALSE}
library(readr)
library(ggplot2)
library(dplyr)
library(rms)
library(synthpop)
library(tidyr)
library(plotly)
library(sjPlot)
source('./helper functions.R')
```

Reading in original dataset, performing multiple imputation and writing out imputed dataset. This chunk commented out because we've already done this and saved the resulting file as our starting point.
```{r echo=FALSE, warning=FALSE}
# 
# brinati_covid_study_v2 <- read_csv("data/brinati-covid_study_v2.csv",
# col_types = cols(GENDER = col_factor(levels = c("M",
# "F")), SWAB = col_factor(levels = c("0",
# "1"))))
# brinati_covid_study_v2 <- brinati_covid_study_v2 %>% mutate_if(is.numeric, .funs = function(x) {x+0.001})
# library(mi)
# brainati.mi <- missing_data.frame(as.data.frame(brinati_covid_study_v2), favor_positive = TRUE)
# 
# 
# brainati.mi<-change(brainati.mi, y=c('Lymphocytes', 'Basophils', 'Monocytes','Eosinophils', 'Basophils'), what='type', to='positive-continuous')
# show(brainati.mi)
# image(brainati.mi)
# 
# options(mc.cores = 4)
# imputations <- mi(brainati.mi, n.iter = 90, n.chains = 4, max.minutes = 20) 
# show(imputations)
# round(mipply(imputations, mean, to.matrix = TRUE), 3)
# Rhats(imputations)
# dfs <- complete(imputations, m=2)
# brinati_covid_v2.imputed <- dfs[[1]][,1:16]
# write.csv(brinati_covid_v2.imputed, file='./data/brinati-covid_study_v2_imputed.csv', row.names = FALSE)
```

Read in dataset from Brinati paper. Dataset already been imputed based on code above. 
```{r}
require(readr)
brinati_covid_study_v2.imputed <- read_csv("data/brinati-covid_study_v2_imputed.csv",
  col_types = cols(GENDER = col_factor(levels = c("M",
  "F")), SWAB = col_factor(levels = c("0",
  "1"))))
```


```{r}

glm.orig.fit<-glm(SWAB ~ ., brinati_covid_study_v2.imputed, family='binomial')
glm.orig.fit
write.csv(summary(glm.orig.fit)$coef, file='output/OR-original-model.csv', row.names = FALSE)
predicted = plogis(predict(glm.orig.fit))
observed = brinati_covid_study_v2.imputed$SWAB
assessPerf(predicted, observed)
p<-plot_model(glm.orig.fit)
p<-ApplyFigureThemeLargeFontOnly(p + ylim(.1, 2.5) ) 
  
p
ggsave(filename = 'figs/OR-original-model.png', plot=p, width=6, height=6, units='in', dpi=300)
```

# Draw ROC curve
```{r}
cms.cutoffs <- lapply(seq(0.01,0.99,0.01), function(cutoff) {
  ret=confusionMatrix(predicted, observed, cutoff=cutoff)
  ret$cutoff = cutoff
  ret
  })
ff<-data.frame(t(sapply(cms.cutoffs, function(cf) c("Cutoff"=cf$cutoff, "Sensitivty"=cf$sens, "Specificity"=cf$spec))))
colnames(ff) <- c('Cutoff', 'Sensitivity','Specificity')


cutoffs_to_plot <- c(0.09, 0.3, 0.6, 0.9)
p <- ApplyFigureTheme(ff %>% mutate(fpr=1-Specificity) %>% filter(Cutoff %in% cutoffs_to_plot) %>%
  ggplot(., aes(x=fpr, y=Sensitivity)) + 
    geom_point(size=3, color='darkblue') + xlim(0,1) + ylim(0,1)+
    xlab('False Positive Rate\n(1-Specificity)') + ylab('True Positive Rate\n(Sensitivity)')
  )
ggsave(filename = 'figs/example-sens-spec-on-roc.png', plot=p, width=6, height=6, units='in', dpi=300)

for (cutoff in lapply(1:length(cutoffs_to_plot), function(i) cutoffs_to_plot[1:i])) {
  p <- ApplyFigureTheme(ff %>% mutate(fpr=1-Specificity) %>% filter(Cutoff %in% cutoff) %>%
    ggplot(., aes(x=fpr, y=Sensitivity)) + 
      geom_point(size=3, color='darkblue') + xlim(0,1) + ylim(0,1)+
      xlab('False Positive Rate\n(1-Specificity)') + ylab('True Positive Rate\n(Sensitivity)')
  )
  ggsave(filename = paste0('figs/example-sens-spec-on-roc-', cutoff[length(cutoff)],  '.png'), 
         plot=p, width=6, height=6, units='in', dpi=300)
}
p <- ApplyFigureTheme(
  ggplot(ff, aes(x=1-Specificity, y=Sensitivity)) + geom_line()+ 
    geom_ribbon(aes(ymin = 0, ymax=Sensitivity), color=NA, fill='blue',alpha=0.3) + 
    xlim(0,1) + ylim(0,1)+
      xlab('False Positive Rate\n(1-Specificity)') + ylab('True Positive Rate\n(Sensitivity)') 
)
ggsave(filename = 'figs/example-roc.png', 
         plot=p, width=6, height=6, units='in', dpi=300)
p <- ff %>% mutate(fpr=1-Specificity) %>%
  ggplot(., aes(x=fpr, y=Sensitivity)) + geom_point(aes(frame=Cutoff)) +
    xlab('False Positive Rate (1-Specificity)') + ylab('True Positive Rate\n(Sensitivity)')
  
  
ggplotly(p)
```

# Draw calibration
```{r}
cal_breaks = seq(0, 1, .1)
cal_deciles <- data.frame(Predicted=predicted, Observed = as.integer(as.character(observed))) %>%
  mutate(Bins = cut(Predicted, breaks=cal_breaks, include.lowest = TRUE))
cal_deciles_summary <- cal_deciles %>% group_by(Bins) %>% summarise(n=n(), Average_Observed = mean(Observed),
                                                                    Average_Predicted=mean(Predicted)) 


for (cutoff in lapply(1:nrow(cal_deciles_summary), function(i) cal_deciles_summary$Bins[1:i])) {
  p<-ApplyFigureThemeCalCurvePoints(
    ggplot(cal_deciles_summary %>% filter(Bins %in% cutoff), aes(x=Average_Predicted, y=Average_Observed)) )
  p<-ApplyFigureThemeLargeFontOnly(p)
  SaveStdSquareFigure(p, filename = paste0('figs/example-cal-curve-points-', cutoff[length(cutoff)],  '.png'))
}

p<-ApplyFigureThemeCalCurvePoints(ggplot(cal_deciles_summary , aes(x=Average_Predicted, y=Average_Observed)) )
p<-ApplyFigureThemeLargeFontOnly(p)

SaveStdSquareFigure(p, 'figs/cal-curve-deciles-all.png')

p<-p + geom_smooth(method='lm', se=FALSE)
SaveStdSquareFigure(p, 'figs/cal-curve-deciles-all-with-bestfit.png')
p
            
```

Calculate hosmer lemeshow statistics
```{r}
require(performance)
hl.org <- performance_hosmer(glm.orig.fit)
hl.org
```

Create smoothed cal curve from Van hoorde et al
```{r}
p<-CreateSmoothedCalCurvePlot(predicted, observed)
p<-ApplyFigureThemeLargeFontOnly(p)
SaveStdSquareFigure(p, 'figs/smoothed-cal-curve-orig-data.png')
p
```

Boot strap performance in the original dataset
```{r warning=FALSE, echo=FALSE}
df <- as.data.frame(brinati_covid_study_v2.imputed)
nBoot = 100
set.seed(1342349)
trainIdxBoot <- lapply(1:nBoot, function(i) sample(1:nrow(df), size=nrow(df), replace=TRUE))

bootPerf.origdata <- lapply(trainIdxBoot, 
                   function(trainIdx) 
                     assessSingleTrainTest(trainIdx, (1:nrow(df))[-trainIdx], df, glm.model = SWAB~ ., outcome.var.name = 'SWAB')
                   ) 

measures = c('C (ROC)', 'Brier', 'Intercept', 'Slope')
brinati.orig.boot.perf <- data.frame(matrix(
  unlist(lapply(bootPerf.origdata, function(boot) boot$test.perf)), 
  nrow = nBoot, byrow = TRUE))
colnames(brinati.orig.boot.perf) <- names(bootPerf.origdata[[1]]$test.perf)

brinati.orig.boot.perf <- gather(brinati.orig.boot.perf, Measure, Value, Dxy:`S:p`, factor_key = TRUE)
p<-ggplot(brinati.orig.boot.perf %>% filter(Measure %in% measures), aes(x=Measure, y=Value,  color=Measure)) + geom_boxplot() +
  geom_point(data=data.frame(Measure = measures, Value = c(1, 0, 0, 1)), size=3, color='blue') + theme_bw()
p<-ApplyFigureTheme(p)
SaveStdSquareFigure(p, 'figs/orig-model-bootstrap-evaluation.png')
p
```


Generate new a synthetic dataset with a prevalence of 10%
```{r}

brinati.syn.factory <- generateSyntheticDataFactory(brinati_covid_study_v2.imputed, method = 'syn')
brinati.syn.prev10 <- brinati.syn.factory(prev = 0.10, sampSize = 279)


#Assess performance of the original model on new prevalence 10 data
assessPerf(predicted = plogis(predict(glm.orig.fit, newdata=as.data.frame(brinati.syn.prev10))), 
           observed = brinati.syn.prev10$SWAB)

# refit the model
glm.syn.fit<-glm(SWAB ~ ., brinati.syn.prev10, family='binomial')
glm.syn.fit
```
Generate new a synthetic dataset with a prevalence of 63% -- close to the original dataset
```{r}

brinati.syn.factory <- generateSyntheticDataFactory(brinati_covid_study_v2.imputed, method = 'boot')
brinati.syn.prev10 <- brinati.syn.factory(prev = 0.1, sampSize = 279) %>% select(-weight)


#Assess performance of the original model on new prevalence 10 data
assessPerf(predicted = plogis(predict(glm.orig.fit, newdata=as.data.frame(brinati.syn.prev10))), 
           observed = brinati.syn.prev10$SWAB)

# refit the model
glm.syn.fit<-glm(SWAB ~ ., brinati.syn.prev10, family='binomial')
glm.syn.fit
```

Boot strap performance of a complete re-moel of a prevalence 10% dataset
```{r echo=FALSE, warning=FALSE}
glm.model = SWAB ~ . 
nBoot = 100
df <- as.data.frame(brinati.syn.prev10)
set.seed(1342349)
trainIdxBoot <- lapply(1:nBoot, function(i) sample(1:nrow(df), size=nrow(df), replace=TRUE))

bootPerf.prev10 <- lapply(trainIdxBoot, 
                   function(trainIdx) 
                     assessSingleTrainTest(trainIdx, (1:nrow(df))[-trainIdx], df, glm.model, outcome.var.name = 'SWAB')
                   ) 
auc.boot <- sapply(bootPerf.prev10, function(boot) boot$test.perf['C (ROC)'])
sprintf("AUC: %0.2f (%0.2f, %0.2f)", median(auc.boot), quantile(auc.boot, probs=c(0.025)), quantile(auc.boot, probs=c(0.975)))
```

II. Look at performance as a function of prevalence
```{r}
prevalences = seq(0.01, 0.99, 0.01)
dfs_prevs <- lapply(prevalences, function (prev) brinati.syn.factory(prev = prev))
dfs.prevs.perf <- lapply(dfs_prevs, function(df) {
  assessPerf(predicted = plogis(predict(glm.orig.fit, newdata=as.data.frame(df))), observed = df$SWAB)
})
```

Calculate the different validation measures
```{r}

dfs.prevs.perf.df <- data.frame(matrix(unlist(dfs.prevs.perf), nrow = length(prevalences), byrow = TRUE))
colnames(dfs.prevs.perf.df) <- names(dfs.prevs.perf[[1]])
dfs.prevs.perf.df$Prevalence = prevalences
dfs.prevs.perf.df <- gather(dfs.prevs.perf.df, Measure, Value, Dxy:`S:p`, factor_key = TRUE)
```

Plot the distribution of validation measures
```{r}
measures = c('C (ROC)', 'Brier', 'Intercept', 'Slope')
ggplot(dfs.prevs.perf.df %>% filter(Measure %in% measures), aes(x=Measure, y=Value,  color=Measure)) + geom_boxplot() +
  geom_point(data=data.frame(Measure = measures, Value = c(1, 0, 0, 1)), size=3, color='blue') + theme_bw()

ggplot(dfs.prevs.perf.df %>% filter(Measure %in% measures), aes(x=Prevalence, y=Value,  color=Measure, group=Measure)) + geom_line() + theme_bw()
```

